Observation streamlining apparatus, observation streamlining method and program

ABSTRACT

An observation streamlining apparatus includes one or more computers each including a mempry and a processor configured to discriminate between an observation-necessary time slot and an observation-unnecessary time slot with an intervention measure including at least a time when a predetermined intervention is performed on a user as an input, the observation-necessary time slot indicating a time slot when a user’s action or state needs to be observed, and the observation-unnecessary time slot indicating a time slot when the user’s action or state does not need to be observed; and execute predetermined processing for observing the user’s action or state when the observation-necessary time slot arrives.

TECHNICAL FIELD

The present disclosure relates to an observation streamlining apparatus,an observation streamlining method and a program.

BACKGROUND ART

An increase in lifestyle-related diseases is a social issue, and many ofthem result from accumulation of unhealthy lifestyle habits. In order toprevent lifestyle-related diseases, it is effective to review one’slifestyle before getting sick and to adopt healthy habits such asadequate sleep, proper exercise, and regular eating habits.

Thus, in recent years, applications that dynamically promote users toperform some action such as sleep, relaxation, or exercise (that is,applications that dynamically perform intervention of promoting users toperform some action) have become known. In order to achieve suchintervention, appropriate intervention is required to be determinedwhile observing an action and state of a user (hereinafter, an actionand a state of a user will be collectively referred to as a “useraction”) (NPL 1).

CITATION LIST Non Patent Literature

NPL 1: Rabbi, Mashfiqui, et al. “Automated personalized feedback forphysical activity and dietary behavior change with mobile phones: arandomized controlled trial on adults.” JMIR mHealth and uHealth 3.2(2015)

SUMMARY OF THE INVENTION Technical Problem

However, in the related art, it is not possible to efficiently observe auser action. For example, in order to determine appropriateintervention, a system needs to observe a user action at all times, buta user action is not always possible to be observed. Further, in a casewhere a user action which is difficult for a system to recognizeautomatically is set to be an observation target, a user action needs tobe described manually by a human.

An embodiment of the present disclosure has been made in view of theabove-described circumstances, and an object thereof is to efficientlyobserve a user action.

Means for Solving the Problem

In order to accomplish the above-mentioned object, an observationstreamlining apparatus according to an embodiment includes adiscrimination unit that discriminates between an observation-necessarytime slot, which indicates a time slot when a user’s action or stateneeds to be observed, and an observation-unnecessary time slot, whichindicates a time slot when the user’s action or state does not need tobe observed, with an intervention measure including at least a time whena predetermined intervention is performed on a user as an input, and anobservation promotion unit that executes predetermined processing forobserving the user’s action or state when the observation-necessary timeslot arrives.

Effects of the Invention

According to an aspect of the present invention, it is possible toefficiently observe a user action.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a functionalconfiguration of an observation streamlining apparatus according toExample 1.

FIG. 2 is a diagram illustrating an example of action log data inExample 1.

FIG. 3 is a diagram illustrating an example of goal data in Example 1.

FIG. 4 is a diagram illustrating an example of intervention measure datain Example 1.

FIG. 5 is a flowchart illustrating an example of a processing flowexecuted by the observation streamlining apparatus in Example 1.

FIG. 6 is a diagram illustrating an example of discrimination between anobservation-necessary time slot and an observation-unnecessary timeslot.

FIG. 7 is a diagram illustrating an example of a user action inputscreen.

FIG. 8 is a diagram illustrating an example of experimental resultsindicating that there are an observation-necessary time slot and anobservation-unnecessary time slot.

FIG. 9 is a diagram illustrating an example of a functionalconfiguration of an observation streamlining apparatus in Example 2.

FIG. 10 is a diagram illustrating an example of action log data inExample 2.

FIG. 11 is a diagram illustrating an example of intervention measuredata in Example 2.

FIG. 12 is a flowchart illustrating an example of a processing flowexecuted by the observation streamlining apparatus in Example 2.

FIG. 13 is a diagram illustrating an example of a hardware configurationof an observation streamlining apparatus according to an embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the present disclosure will be described.In the present embodiment, an observation streamlining apparatus 10capable of efficiently observing a user action is described. A useraction is an action of the user, a state of the user, or the like.

Here, in the present embodiment, as an example, an application forperforming intervention for promoting a user action (for example, “Whydon’t you have dinner soon?” or “It’s time to go to bed”) on a user’sgoal (for example, “sleep at 11 p.m.” or the like) is assumed, and acase where a user action for appropriately determining such interventionis efficiently observed will be described. The intervention involves topromoting a user to perform some action. In the present embodiment, asan example, intervention is performed to promote a user to perform auser action. Note that such intervention is achieved by, for example, areminder notification or the like.

Further, in the present embodiment, as an example, it is assumed thatuser actions are 1: sleep, 2: breakfast, 3: lunch, 4: dinner, 5: snack,6: go to work, 7: work, 8: get off work, 9: housework, 10: exercise, 11:relax, 12: bath, 13: hobby, 14: drink, and 15: shopping. On the otherhand, intervention (more precisely, a user action promoted byintervention) also includes 16: no intervention (none), in addition to 1to 15 described above.

Example 1

Hereinafter, Example 1 will be described. In Example 1, a user action isassumed to be State, intervention by an agent (that is, a system such asan application) is assumed to be Action, and a user’s goal is assumed tobe Reward. In Example 1, a case where an optimal intervention measure(hereinafter referred to as an “intervention measure”) is learned bymodel-based reinforcement learning, and then the efficiency ofobservation for determining an optimal intervention by the interventionmeasure is improved, will be described.

In the model-based reinforcement learning, environmental parameters suchas a state transition probability are estimated, and then anintervention measure is estimated using the environmental parameters. Inthe following, a time slot having a predetermined time interval (in thepresent example, an interval of one hour) will be shown by assuming anindex representing the time in reinforcement learning to be t.Specifically, it is assumed that t = 0 represents a time slot of 0:00 to0:59, t = 1 represents a time slot of 1:00 to 1:59, ..., and t = 23represents a time slot of 23:00 to 23:59.

Functional Configuration of Observation Streamlining Apparatus 10(Example 1)

First, a functional configuration of the observation streamliningapparatus 10 in Example 1 will be described with reference to FIG. 1 .FIG. 1 is a diagram illustrating an example of the functionalconfiguration of the observation streamlining apparatus 10 in Example 1.

As illustrated in FIG. 1 , the observation streamlining apparatus 10 inExample 1 includes a transition probability estimation unit 101, alearning unit 102, a discrimination unit 103, and an action acquisitionpromotion unit 104.

The transition probability estimation unit 101 estimates a statetransition probability in the model-based reinforcement learning, withaction log data representing a log of user actions collected in advance,as an input.

The learning unit 102 estimates intervention measure data representingan optimal intervention measure, with goal data representing a user’sgoal and the state transition probability estimated by the transitionprobability estimation unit 101, as inputs.

The discrimination unit 103 discriminates between a time slot in whichit is necessary to observe a user action (hereinafter referred to as an“observation-necessary time slot”) and a time slot in which it isunnecessary to observe a user action (hereinafter referred to as an“observation-unnecessary time slot”) with the intervention measure dataestimated by the learning unit 102, as an input. Specifically, thediscrimination unit 103 determines a time slot in which an optimalintervention varies depending on a user action to be theobservation-necessary time slot, and determines a time slot in which anoptimal intervention is identical regardless of a user action to be theobservation-unnecessary time slot.

The action acquisition promotion unit 104 performs various processingoperations for observing a user action during the observation-necessarytime slot.

For example, the action acquisition promotion unit 104 displays a screenfor promoting a user to input a user action (hereinafter referred to asa “user action input screen”) on a terminal or the like that is used bythe user. In addition, the action acquisition promotion unit 104 mayoutput, for example, an alert for promoting the user to input a useraction to the terminal or the like. In addition, for example, the actionacquisition promotion unit 104 may make a frequency of promoting a userto input a user action vary between an observation-necessary time slotand an observation-unnecessary time slot (that is, an input is promotedat a high frequency during the observation-necessary time slot, while aninput is promoted at a low frequency during the observation-unnecessarytime slot, or the like). Further, for example, in a case where a useraction can be automatically observed by a sensor or the like, the actionacquisition promotion unit 104 may observe a user action during theobservation-necessary time slot but may not observe a user action duringthe observation-unnecessary time slot. In addition, for example, in acase where a user forgets to input a user action during theobservation-necessary time slot or in a case where a user action cannotbe observed due to a sensor error or the like during theobservation-necessary time slot, the action acquisition promotion unit104 may output the above-mentioned alert to the terminal or the like.

Note that, in the present embodiment, a case where the actionacquisition promotion unit 104 displays a user action input screen onthe terminal or the like will be described as an example.

Here, an example of action log data in Example 1 will be described withreference to FIG. 2 . FIG. 2 is a diagram illustrating an example ofaction log data in Example 1.

As illustrated in FIG. 2 , the action log data in Example 1 is data inwhich a time is associated with a user action that is actually performedby a user. In the example illustrate in FIG. 2 , a time “9:00” isassociated with a user action “breakfast”, a time “10:00” is associatedwith a user action “work”, a time “11:45” is associated with a useraction “lunch”, a time “13:00” is associated with a user action “work”,and a time “15:30” is associated with a user action “snack”. Theyindicate that the user is performing the user action “breakfast” between9:00 and 10:00, the user is performing the user action “work” between10:00 and 11:45, the user is performing the user action “lunch” between11:45 and 13:00, and the user is performing the user action “work”between 13:00 and 15:30.

In this manner, the action log data in Example 1 is data in which a useraction that is actually performed by the user is associated with a timewhen the user action is performed. Such action log data is collected inadvance before a state transition probability in model-basedreinforcement learning is estimated.

Next, an example of goal data in Example 1 will be described withreference to FIG. 3 . FIG. 3 is a diagram illustrating an example ofgoal data in Example 1.

As illustrated in FIG. 3 , the goal data in Example 1 is data in which atime is associated with a user action that the user has set as a goal atthat time. In the example illustrated in FIG. 3 , a time “22:00” and auser action “sleep” are associated with each other. This indicates thatthe user has a goal of performing a user action “sleep” at 22:00 (thatis, a goal of going to bed at 22:00). Intervention measure data to bedescribed below is data representing an optimal intervention measure forachieving this goal.

In this manner, the goal data in Example 1 is data in which a user’sgoal time is associated with a user action at that time. As will bedescribed below, a reward for model-based reinforcement learning isdefined by goal data.

Next, an example of intervention measure data in Example 1 will bedescribed with reference to FIG. 4 . FIG. 4 is a diagram illustrating anexample of intervention measure data in Example 1.

As illustrated in FIG. 4 , the intervention measure data in Example 1 isdata in which a time, a user action, and an optimal intervention in acase where the user action is performed at the time are associated witheach other. In the example illustrated in FIG. 4 , a time “20:00”, auser action “dinner”, and an intervention “bath” are associated witheach other, and the time “20:00”, a user action “housework”, and theintervention “bath” are associated with each other. This indicates that,in a case where the user action “dinner” or “housework” is observed at20:00, an optimal intervention is the intervention for promoting a useraction “bath”.

Similarly, in the example illustrated in FIG. 4 , a time “20:00”, a useraction “hobby”, and an intervention “relax” are associated with eachother, and the time “20:00”, a user action “work”, and the intervention“relax” are associated with each other. This indicates that, in a casewhere the user action “hobby” or “work” is observed at 20:00, an optimalintervention is the intervention for promoting a user action “relax”.

Similarly, in the example illustrated in FIG. 4 , a time “20:00”, a useraction “sleep”, and an intervention “no intervention” are associatedwith each other. This indicates that, in a case where the user action“sleep” is observed at 20:00, an optimal intervention is “nointervention”.

In this manner, the intervention measure data in Example 1 is datarepresenting an optimal intervention for achieving a user’s goal (thatis, the detail of the optimal intervention) in a case where a certainuser action is observed at a certain time.

Processing Flow Executed by Observation Streamlining Apparatus 10(Example 1)

Next, a processing flow executed by the observation streamliningapparatus 10 in Example 1 will be described with reference to FIG. 5 .FIG. 5 is a flowchart illustrating an example of a processing flowexecuted by the observation streamlining apparatus in Example 1.

First, the transition probability estimation unit 101 estimates a statetransition probability in model-based reinforcement learning with actionlog data as an input (step S101). Note that the transition probabilityestimation unit 101 estimates a state transition probability by anymethod. For example, each user action (that is, each state) can beexpressed as a discrete value, and thus the transition probabilityestimation unit 101 can estimate a state transition probability bycounting combinations of a state s_(t) and the next state s_(t+1) ateach time index t. Note that the state s_(t) is a user action at a timeindex t and represents s_(t) = 1 (sleep), s_(t) = 2 (breakfast), s_(t) =3 (lunch), s_(t) = 4 (dinner), s_(t) = 5 (snack), s_(t) = 6 (go towork), s_(t) = 7 (work), s_(t) = 8 (get off work), s_(t) = 9(housework), st = 10 (exercise), s_(t) = 11 (relax), s_(t) = 12 (bath),s_(t) = 13 (hobby), s_(t) = 14 (drink), and s_(t) = 15 (shopping).

Next, the learning unit 102 estimates intervention measure data withgoal data, and the state transition probability estimated in step S101described above as inputs (step S102). The learning unit 102 estimates ameasure for increasing the sum of rewards defined by goal data as muchas possible in the future (that is, an optimal measure) by a knownmethod. Thereby, intervention measure data representing this measure isobtained. Here, the reward may be defined as larger value in a casewhere the user’s goal has been achieved, but it is conceivable that areward is defined to give, for example, a large positive value r_(g) ina case where the user’s goal has been achieved, 0 in the case of nointervention, and a negative value r_(itv) in other cases.

Specifically, for example, it is conceivable that R_(t)(s_(t), a_(t),s_(t) + ₁) = r_(g)I(s_(t) + ₁ = s_(g), t + 1 = t_(g)) + r_(itv)I (at ≠16) is defined by setting a target state to be s_(g), setting a timeindex representing a time slot in which the target state s_(g) isachieved to be t_(g), setting a reward at the time index t to be R_(t),and setting an intervention to be at. Here, I(·) is an indicatorfunction. Note that at = 1, ..., 15 are interventions for promoting useractions “sleep”, “breakfast”, “lunch”, “dinner”, “snack”, “go to work”,“work”, “get off work”, “housework”, “exercise”, “relax”, “bath”,“hobby”, “drink”, and “shopping”, respectively, and at = 16 indicates nointervention (none).

Note that a user may set a goal without designating a time (for example,a case where a period of time of a specific user action such as exerciseis desired to be increased, or the like). In this case, it isconceivable that the reward R_(t) is defined as R_(t)(s_(t), a_(t),s_(t) + ₁) = r_(g)I(s_(t) + ₁ = s_(g)) + r_(itv)I (a_(t) ≠ 16).

Next, the discrimination unit 103 discriminates whether each time slotis an observation-necessary time slot or an observation-unnecessary timeslot with the intervention measure data estimated in step S102 describedabove as an input. (step S103). Specifically, the discrimination unit103 determines a time slot in which an optimal intervention variesdepending on a user action to be an observation-necessary time slot anddetermines a time slot in which an optimal intervention is identicalregardless of a user action to be an observation-unnecessary time slot.

Here, as an example, state transition probabilities visualized withshading are illustrated in FIG. 6 where states (States) at times t = 20and 21 (that is, time slots of 20:00 to 20:59 and 21:00 to 21:59) areshown on a vertical axis, and optimal interventions (Actions) are shownon a horizontal axis.

In the left drawing of FIG. 6 , it illustrates that, in a case where astate s₂₀ is “dinner” or “bath”, an optimal intervention a₂₀ is “relax”,in a case where the state s₂₀ is “relax”, “hobby”, or “shopping”, anoptimal intervention a₂₀ is “dinner”, and in a case where the state s₂₀is something else, the optimal intervention a₂₀ is “no intervention(none)”. That is, when t = 20, an optimal intervention a_(t) variesdepending on the state s_(t) (that is, a user action). For this reason,a time slot of 20:00 to 20:59 represented by t = 20 is determined to bean observation-necessary time slot. This is because it is necessary toobserve a user action, because an optimal intervention varies dependingon a user action.

On the other hand, in the right drawing of FIG. 6 , an optimalintervention a₂₁ is “relax” even when a state s₂₁ is any user action.That is, when t = 21, an optimal intervention at is identical regardlessof a state s_(t) (that is, a user action). For this reason, a time slotof 21:00 to 21:59 represented by t = 21 is determined to be anobservation-unnecessary time slot. This is because it is not necessaryto observe a user action, because an optimal intervention is identicalregardless of a user action.

Next, when the observation-necessary time slot arrives, the actionacquisition promotion unit 104 displays a user action input screen on aterminal or the like which is used by the user (step S104). Here, anexample of the user action input screen displayed on the terminal or thelike is illustrated in FIG. 7 . A user action input screen 1000illustrated in FIG. 7 is a screen for promoting an input of a useraction during a time slot of 20:00 to 20:59. The user action inputscreen 1000 illustrated in FIG. 7 includes a user action selection field1100, and a user can input a user action during the time slot byselecting a desired user action in the user action selection field 1100.Information indicating a user action which is input by the user istransmitted to the observation streamlining apparatus 10, and an optimalintervention in a case where the user action is performed during thetime slot is determined based on intervention measure data.

Note that the action acquisition promotion unit 104 may display the useraction input screen on the terminal or the like when theobservation-necessary time slot arrives (that is, in a case where thestart time of the observation-necessary time slot has arrived), and maydisplay the user action input screen on the terminal or the like duringthe observation-necessary time slot or at the end time of theobservation-necessary time slot.

As described above, first, the observation streamlining apparatus 10 inExample 1 learns an optimal intervention measure by model-basedreinforcement learning. Next, the observation streamlining apparatus 10in Example 1 discriminates between the observation-necessary time slotin which it is necessary to observe a user action (State) and theobservation-unnecessary time slot in which it is not necessary toobserve a user action, in order to determine an optimal intervention(Action). Thereby, it is unnecessary to observe a user action during theobservation-unnecessary time slot, and a user action only needs to beobserved during the observation-necessary time slot, and thus it ispossible to achieve the efficient observation of a user action.

Note that the inventor of the present application has confirmed thatthere is the observation-necessary time slot and theobservation-unnecessary time slot by experiment. In the experiment,intervention measure data was estimated by the observation streamliningapparatus 10 in Example 1 using the actual action log data and goal datacollected from a plurality of participants. At this time, visualizedintervention measure data estimated from action log data and goal datacollected from a participant A and visualized intervention measure dataestimated from action log data and goal data collected from aparticipant B are illustrated in FIG. 8 .

The upper left drawing of FIG. 8 illustrates that an optimalintervention a₂₀ is “no intervention (none)” in a case where a state s₂₀indicating a user action of the participant A at t = 20 is “dinner”, andan optimal intervention a₂₀ is “dinner” in a case where the state s₂₀ isnot “dinner”. Thus, in the case of the participant A, it can be saidthat a time slot of 20:00 to 20:59 represented by t = 20 is theobservation-necessary time slot.

On the other hand, in the lower left drawing of FIG. 8 , even when astate s₂₁ representing a user action of the participant A at t = 21 isany user action, an optimal intervention a₂₁ is “bath”. Thus, in thecase of the participant A, it can be said that a time slot of 21:00 to21:59 represented by t = 21 is the observation-unnecessary time slot.

Similarly, the upper right drawing of FIG. 8 illustrates that an optimalintervention a₂₀ is “bath” in a case where a state s₂₀ indicating a useraction of the participant B at t = 20 is “dinner” or “housework”, and anoptimal intervention a₂₀ is “no intervention (none)” in a case where thestate s₂₀ is not “dinner” or “housework”. Thus, in the case of theparticipant B, it can be said that a time slot of 20:00 to 20:59represented by t = 20 is the observation-necessary time slot.

On the other hand, in the lower right drawing of FIG. 8 , even when astate s₂₁ representing a user action of the participant B at t = 21 isany user action, an optimal intervention a₂₁ is “housework”. Thus, inthe case of the participant B, it can be said that a time slot of 21:00to 21:59 represented by t = 21 is the observation-unnecessary time slot.

Example 2

Hereinafter, Example 2 will be described. In Example 2, a case where theefficiency of observation for determining an optimal intervention isimproved by optimizing a timing at which an intervention is performedwill be described. For example, Bayesian optimization or the like can beapplied to optimize the timing.

Note that, in Example 2, differences from Example 1 will be mainlydescribed, and the description of components similar to those in Example1 will be omitted.

Functional Configuration of Observation Streamlining Apparatus 10(Example 2)

First, a functional configuration of an observation streamliningapparatus 10 in Example 2 will be described with reference to FIG. 9 .FIG. 9 is a diagram illustrating an example of a functionalconfiguration of the observation streamlining apparatus 10 in Example 2.

As illustrated in FIG. 9 , the observation streamlining apparatus 10 inExample 2 includes a discrimination unit 103, an action acquisitionpromotion unit 104, and a modeling unit 105. Note that the actionacquisition promotion unit 104 is similar to that in Example 1, and thusdescription thereof will be omitted.

The modeling unit 105 estimates intervention measure data representing atiming (time) at which an intervention is performed with action log datarepresenting a log sequence of user actions and times collected inadvance and a reward value in a case where a predetermined interventionis performed on this log sequence as an input. Note that the rewardvalue is a value representing the goodness of intervention for achievinga predetermined goal.

The discrimination unit 103 discriminates between anobservation-necessary time slot and an observation-unnecessary time slotin the same manner as in Example 1 with the intervention measure dataestimated by the modeling unit 105 as an input.

Here, an example of action log data in Example 2 will be described withreference to FIG. 10 . FIG. 10 is a diagram illustrating an example ofaction log data in Example 2.

As illustrated in FIG. 10 , the action log data in Example 2 is data inwhich a log sequence of user actions and times is associated with areward value in a case where a predetermined intervention is performedon the log sequence. In the example illustrated in FIG. 10 , a logsequence “12:00 lunch, 13:00 work, 18:00 dinner” is associated with areward value “30.5”. This indicates that a reward (that is, the goodnessof intervention for achieving a predetermined goal) is “30.5” in a casewhere a user has performed a predetermined intervention while performinga user action “dinner” after 18:00 on the assumption that the user isperforming a user action “lunch” between 12:00 and 12:59 and that theuser is performing a user action “work” between 13:00 and 17:59. Thisreward means that “a sleep time has extended by 30.5 hours”, forexample, in a case where a predetermined goal is “securing of a sleeptime”.

In this manner, the action log data in Example 2 is data in which a logsequence of user actions is associated with a reward in a case where apredetermined intervention has been performed on the log sequence.

Next, an example of the intervention measure data in Example 2 will bedescribed with reference to FIG. 11 . FIG. 11 is a diagram illustratingan example of intervention measure data in Example 2.

As illustrated in FIG. 11 , the intervention measure data in Example 2is time-series data indicating an optimal intervention timing. In theexample illustrated in FIG. 11 , the intervention measure data includestimes “16:00” and “20:00”. This indicates that “16:00” and “20:00” areoptimal intervention timings for performing a predeterminedintervention.

In this manner, the intervention measure data in Example 2 istime-series data representing an optimal intervention timing.

Processing Flow Executed by Observation Streamlining Apparatus 10(Example 2)

Next, a processing flow executed by the observation streamliningapparatus 10 in Example 2 will be described with reference to FIG. 12 .FIG. 12 is a flowchart illustrating an example of a processing flowexecuted by the observation streamlining apparatus in Example 2.

First, the modeling unit 105 estimates intervention measure data withaction log data as an input (step S201). Note that, as described above,in Example 2, the action log data is data representing a log sequence ofuser actions and times and a reward value in a case where apredetermined intervention is performed on the log sequence, and theintervention measure data is data representing an optimal interventiontiming.

Here, the modeling unit 105 estimates intervention measure data bymodeling a correspondence relationship between the log sequence of useractions and times and a reward value thereof. A Gaussian process iswidely used for modeling, but the modeling can also be achieved by othermethods such as a Gaussian process using a Poisson process for noise. Avector having a fixed length is mainly handled as an input in a Gaussianprocess. However, in a case where the number of user actions and timesincluded in the log sequence is not a fixed length as in the presentexample, a linear function kernel is used. Modeling is performed in thismanner, and thus it is possible to predict a reward in a case where apredetermined intervention will be performed at a future time. Thus, atime at which a reward is largest is output as intervention measuredata. Note that, for the linear function kernel, reference will be madeto, for example, “Park, Il Memming, et al. “Kernel methods on spiketrain space for neuroscience: a tutorial.” IEEE Signal ProcessingMagazine 30.4 (2013): 149-160" and the like.

Next, the discrimination unit 103 discriminates between anobservation-necessary time slot and an observation-unnecessary time slotwith the intervention measure data estimated in step S201 describedabove as an input (step S202). Because a user action from the presenttime to an intervention timing does not need to be observed, thediscrimination unit 103 determines a time slot from the present time tothe intervention timing to be the observation-unnecessary time slot anddetermines the other time slots to be the observation-necessary timeslot.

Next, similarly to step S104 in FIG. 5 , the action acquisitionpromotion unit 104 displays a user action input screen on a terminal orthe like which is used by a user when the observation-necessary timeslot arrives (step S203).

As described above, the observation streamlining apparatus 10 in Example2 estimates an optimal intervention timing as an intervention measure byBayesian optimization or the like and then determines a time slot fromthe present time to the optimal intervention timing to be theobservation-unnecessary time slot. Thereby, similarly to Example 1, auser action does not need to be observed during theobservation-unnecessary time slot, and a user action only need beobserved during the observation-necessary time slot, and thus it ispossible to achieve the efficient observation of a user action.

Hardware Configuration

Finally, a hardware configuration of the observation streamliningapparatus 10 according to the present embodiment will be described withreference to FIG. 13 . FIG. 13 is a diagram illustrating an example of ahardware configuration of the observation streamlining apparatus 10according to the embodiment.

As illustrated in FIG. 13 , the observation streamlining apparatus 10according to the present embodiment is a general-purpose computer orcomputer system, and includes an input device 201, a display device 202,an external I/F 203, a communication I/F 204, a processor 205, and amemory device 206. The pieces of hardware are communicably connected viaa bus 207.

The input device 201 is, for example, a keyboard, a mouse, or a touchpanel. The display device 202 is, for example, a display or the like.Note that the observation streamlining apparatus 10 does not need toinclude at least one of the input device 201 or the display device 202.

The external I/F 203 is an interface for an external device. Examples ofthe external device include a recording medium 203 a and the like. Theobservation streamlining apparatus 10 can perform reading, writing, andthe like on the recording medium 203 a via the external I/F 203. In therecording medium 203 a, for example, one or more programs forimplementing the functional units (for example, the transitionprobability estimation unit 101, the learning unit 102, thediscrimination unit 103, and the action acquisition promotion unit 104in the case of Example 1, and the discrimination unit 103, the actionacquisition promotion unit 104, and the modeling unit 105 in the case ofExample 2) included in the observation streamlining apparatus 10 may bestored.

Note that examples of the recording medium 203 a include a compact disc(CD), a digital versatile disk (DVD), a secure digital memory card (SDmemory card), a universal serial bus (USB) memory card, and the like.

The communication I/F 204 is an interface for connecting the observationstreamlining apparatus 10 to a communication network. Note that one ormore programs for implementing the functional units of the observationstreamlining apparatus 10 may be acquired (downloaded) from apredetermined server device or the like via the communication I/F 204.

The processor 205 is any of various calculation devices such as acentral processing unit (CPU) or a graphics processing unit (GPU). Forexample, the functional units included in the observation streamliningapparatus 10 are implemented by processing for causing the processor 205to execute one or more programs stored in the memory device 206.

The memory device 206 is any of various storage devices such as a harddisk drive (HDD), a solid state drive (SSD), a random access memory(RAM), a read only memory (ROM), and a flash memory. Note that variouspieces of data (for example, goal data, action log data, interventionmeasure data, and the like) are stored in, for example, the memorydevice 206.

The observation streamlining apparatus 10 according to the presentembodiment has the hardware configuration illustrated in FIG. 13 andthus can implement the above-mentioned various processing operations.Note that the hardware configuration illustrated in FIG. 13 is anexample, and the observation streamlining apparatus 10 may have anotherhardware configuration. For example, the observation streamliningapparatus 10 may include a plurality of processors 205 or may include aplurality of memory devices 206.

The present disclosure is not limited to the above-described embodimentdisclosed specifically, and various modifications or changes,combinations with known techniques, and the like can be made withoutdeparting from the recitation of claims.

REFERENCE SIGNS LIST

-   10 Observation streamlining apparatus-   101 Transition probability estimation unit-   102 Learning unit-   103 Discrimination unit-   104 Action acquisition promotion unit-   105 Modeling unit

1. An observation streamlining apparatus comprising: one or morecomputers each including a mempry and a processor configured to:discriminate between an observation-necessary time slot and anobservation-unnecessary time slot with an intervention measure includingat least a time when a predetermined intervention is performed on a useras an input, the observation-necessary time slot indicating a time slotwhen a user’s action or state needs to be observed, and theobservation-unnecessary time slot indicating a time slot when the user’saction or state does not need to be observed; and execute predeterminedprocessing for observing the user’s action or state when theobservation-necessary time slot arrives.
 2. The observation streamliningapparatus according to claim 1, wherein the intervention measureincludes the time, the user’s action or state at the time, and a detailof the intervention, and the memory and the processor are furtherconfigured to determine a time slot when the detail of the interventionvaries depending on the user’s action or state to be theobservation-necessary time slot, and determine a time slot when thedetail of the intervention is identical regardless of the user’s actionor state to be the observation-unnecessary time slot.
 3. The observationstreamlining apparatus according to claim 1, wherein the time includedin the intervention measure is a time indicating an optimal timing forperforming the intervention, and the memory and the processor arefurther configured to determine a time slot from a present time to thetime indicating the optimal timing to be the observation-unnecessarytime slot, and determine a time slot other than theobservation-unnecessary time slot to be the observation-necessary timeslot.
 4. The observation streamlining apparatus according to claim 1,wherein the predetermined processing includes at least one of processingfor displaying a screen for promoting an input of the user’s action orstate on a terminal used by the user, processing for outputting an alertfor promoting an input of the user’s action or state on the terminal, orprocessing of acquiring the user’s action or state by a sensor.
 5. Anobservation streamlining method executed by a computer including amemory and a processor, the method comprising: discriminating between anobservation-necessary time slot and an observation-unnecessary time slotwith an intervention measure including at least a time when apredetermined intervention is performed on a user as an input, theobservation-necessary time slot indicating a time slot when a user’saction or state needs to be observed, and the observation-unnecessarytime slot indicating a time slot when the user’s action or state doesnot need to be observed; and executing predetermined processing forobserving the user’s action or state when the observation-necessary timeslot arrives.
 6. A non-transitory computer-readable recording mediumhaving computer-readable instructions stored thereon, which whenexecuted cause a computer including a memory and a processor to executerespective operations in the observation streamlining apparatusaccording to claim 1.